Pattern Analysis and Applications

, Volume 18, Issue 3, pp 713–723 | Cite as

Combining spatial and DCT based Markov features for enhanced blind detection of image splicing

Industrial and Commercial Application


Nowadays, it is extremely simple to manipulate the content of digital images without leaving perceptual clues due to the availability of powerful image editing tools. Image tampering can easily devastate the credibility of images as a medium for personal authentication and a record of events. With the daily upload of millions of pictures to the Internet and the move towards paperless workplaces and e-government services, it becomes essential to develop automatic tampering detection techniques with reliable results. This paper proposes an enhanced technique for blind detection of image splicing. It extracts and combines Markov features in spatial and Discrete Cosine Transform domains to detect the artifacts introduced by the tampering operation. To reduce the computational complexity due to high dimensionality, Principal Component Analysis is used to select the most relevant features. Then, an optimized support vector machine with radial-basis function kernel is built to classify the image as being tampered or authentic. The proposed technique is evaluated on a publicly available image splicing dataset using cross validation. The results showed that the proposed technique outperforms the state-of-the-art splicing detection methods.


Multimedia security Image forensics Authentication Forgery detection Image splicing Markov features Support vector machine 


  1. 1.
    Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Invest 10(3):226–245CrossRefMATHGoogle Scholar
  2. 2.
    Borges PVK, Mayer J (2006) Analysis of position based watermarking. Pattern Anal Appl 9(1):70–82MathSciNetCrossRefGoogle Scholar
  3. 3.
    Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRefGoogle Scholar
  4. 4.
    Change CC, Lin CJ (2010) Libsvm a library for support vector machines. Accessed May 2013
  5. 5.
    Chen C, Shi YQ (2008) Jpeg image steganalysis utilizing both intrablock and interblock correlations. In: IEEE international symposium on circuits and systems (ISCAS), pp 3029–3032Google Scholar
  6. 6.
    Chen W, Shi YQ, Su W (2007) Image splicing detection using 2-d phase congruency and statistical moments of characteristic function. Proceeding of SPIE, San JoseCrossRefGoogle Scholar
  7. 7.
    Cox IJ, Miller ML, Bloom JA (2002) Digital watermarking. Morgan Kaufmann Publishers Inc. San Francisco, CAGoogle Scholar
  8. 8.
    Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge university press, CambridgeGoogle Scholar
  9. 9.
    Dong J, Wang W, Tan T, Shi YQ (2009) Run-length and edge statistics based approach for image splicing detection. In: Kim H-J, Katzenbeisser S, Ho ATS (eds) Digital watermarking. Springer, Berlin, pp 76–87Google Scholar
  10. 10.
    Farid H (1999) Detecting digital forgeries using bispectral analysis. In: Technical report.
  11. 11.
    Farid H, Lyu S (2003) Higher-order wavelet statistics and their application to digital forensics. In: Computer vision and pattern recognition workshop (CVPRW03), vol 8, pp 94–94Google Scholar
  12. 12.
    Fu D, Shi YQ, Su W (2006) Detection of image splicing based on Hilbert–Huang transform and moments of characteristic functions with wavelet decomposition. In: Shi YQ, Jeon B (eds) Digital watermarking. Springer, Berlin, pp 177–187Google Scholar
  13. 13.
    Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422CrossRefMATHGoogle Scholar
  14. 14.
    He Z, Sun W, Lu W, Lu H (2011) Digital image splicing detection based on approximate run length. Pattern Recogn Lett 32(12):1591–1597CrossRefGoogle Scholar
  15. 15.
    He Z, Lu W, Sun W (2012) Improved run length based detection of digital image splicing. In: Digital forensics and watermarking, pp 349–360Google Scholar
  16. 16.
    Hearst MA, Dumais S, Osman E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18–28CrossRefGoogle Scholar
  17. 17.
    Li L, Xue J, Wang X, Tian L (2013) A robust approach to detect digital forgeries by exploring correlation patterns. Pattern Anal Appl 1–15Google Scholar
  18. 18.
    Ng TT, Chang SF (2004) A model for image splicing. Proc IEEE Int Conf Image Process 2:1169–1172Google Scholar
  19. 19.
    Ng TT, Chang SF, Sun Q (2004) Blind detection of photomontage using higher order statistics. In: Proceedings of international symposium on circuits and systems (ISCAS), vol 5, pp 688–691Google Scholar
  20. 20.
    Ng TT, Chang SF, Sun Q (2004) A data set of authentic and spliced image blocks. In: ADVENT technical report, Columbia University, pp 203–204Google Scholar
  21. 21.
    Qazi T, Hayat K, Khan S, Madani S, Khan I, Koodziej J, Li H, Lin W, Yow KC, Xu CZ (2013) Survey on blind image forgery detection. Image processing. IET 7(7):660–670Google Scholar
  22. 22.
    Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginners. Multimed Tools Appl 51(1):133–162CrossRefGoogle Scholar
  23. 23.
    Shi YQ, Xuan G, Zou D, Gao J, Yang C, Zhang Z, Chai P, Chen W, Chen C (2005) Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network. In: IEEE international conference on multimedia and expo ICMEGoogle Scholar
  24. 24.
    Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on multimedia and security, pp 51–62Google Scholar
  25. 25.
    Zhao X, Wang S, Li S, Li J (2012) A comprehensive study on third order statistical features for image splicing detection. Digit Forensics Watermarking 243–256Google Scholar
  26. 26.
    Zhongwei H, Lu Wei Sun W (2012) Digital image splicing detection based on markov features in DCT and DWT domain. Pattern Recogn 45(12):4292–4299CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.College of Computer Sciences and EngineeringKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia
  2. 2.Electrical Engineering DepartmentKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia

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